Predict time series from initial non-time dependant parameters
I don't think you need a recurrent neural network for this. Why not just train a feedforward model with angle of attack etc as input and translation velocities as output? The size of the output will depend on how frequently you want updates to the velocities. e.g. if the update rate is 0.5 seconds and the network is predicting 2 seconds in advance it could have 12 outputs, being x,y,z values every 0.5 seconds.
You may need to massage the way you represent the outputs to find the best approach. For example, will the network be predicting absolute velocities or just the initial velocities plus changes to velocity over time? If the latter you can always get back to absolute velocities.